The winners in business have shifted markedly in the past decade—and the keys to success are likely to be very different again in ten years’ time. How should leaders prepare their companies to thrive in a rapidly evolving landscape? What will it take to win the '20s?

AI at Scale: The Next Frontier in Digital Transformation

The new power of AI is changing business as we know it. BCG’s AI@scale methodology allows you to transform your operating model, so you can move beyond isolated AI use cases toward a company-wide program and realize the full value potential.

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AI is triggering shifts in the value pools of entire industries. And it is redefining what it takes for companies to achieve competitive advantage. Yet, even as many companies have begun applying AI solutions with impressive results, few have developed full-scale AI capabilities that are systemic and companywide.

BCG’s research, conducted in conjunction with MIT, has identified clear patterns. And it has revealed that unleashing the true power of AI requires scaling it across the entire business. Yet at this decisive stage, companies hit what BCG has termed the AI paradox:

It is deceptively easy to launch successful AI pilots. But fiendishly hard to move toward AI@scale.

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That’s because AI systems change constantly as they ingest data and learn from it. Seemingly isolated uses cases interact and become entangled.

Overcoming the AI Paradox

To overcome the AI paradox, you must transform your company’s operating model along three dimensions:

Machine Architecture

Organization Structure

People Management

Machine Architecture

For leveraging machine intelligence at scale, you must optimize machine architecture. Because the structure you use for AI creates your competitive advantage, this detail can’t be left to a small set of technical staff.

End-to-end platforms for managing workflow, such as those used at AI pioneers like Google, Uber, and Facebook, introduce discipline across all the entangled AI systems in your company.

Organization Structure

In a world where humans and machines work in concert, your organization structure must be optimized for both.

Deep expertise should be centralized and pooled at headquarters for AI technologies, data governance, platform decisions, and cybersecurity.

Centralized in the business units should be the cross-functional teams and access to the complete business data pool needed to develop AI use cases.

Managed in a decentralized way—at the shop floor, marketplace, and field level—should be any actions supported by AI.

People Management

Despite all the advances in AI technologies, people continue to play an essential role in its successful application. Companies need to hire data scientists and data engineers. And they will need to deploy strategic workforce planning, reskilling, and change management to ensure that AI helps drive a companywide digital transformation.

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Pulling Together the Transformation Program

An AI@scale transformation should occur through a series of top-down and bottom-up actions to create alignment, buy-in, and follow-through. This ensures the successful industrialization of AI across companies and their value chains:

AI Ambition and Maturity Assessment. This top-down step establishes the overall context of the transformation and helps prevent the company from pursuing disconnected AI pilots. The maturity assessment is typically based on a combination of surveys and interviews.

Evaluation of AI Initiatives and the Operating Model. This bottom-up step provides a baseline of current AI initiatives. It should include goals, business cases, accountabilities, work streams, and milestones in addition to an analysis of data management, algorithms, performance metrics, and cybersecurity. A review of the current AI operating model should also be conducted at this stage.

Priority Setting and Gap Analysis. The next top-down step prioritizes AI initiatives, focusing on easy wins and unicorns. This step also identifies the required changes to the operating model.

Outline of AI@scale Transformation Program. This top-down step consists of both the transformation roadmap, including the order of initiatives to be rolled out, and the creation of a program management office to oversee the transformation.

Detailed Implementation Planning of AI@scale Program. The last step covers implementation, detailing the work streams, responsibilities, targets, milestones, and resources.

By systematically moving through these steps, the implementation of digital transformation will proceed with much greater speed and certainty. Still, companies must be aware that the transformation can take one to three years, depending on the complexity of the overall organization.

Retailers have big hopes for artificial intelligence since it can personalize and enhance the shopping experience in new ways, but challenges arise in building the data and analytics platform needed to support AI.

As intelligent machines proliferate, their much faster decisions and actions hit the physical limits of synchronization throughout space and time. In nature, the distributed intelligence of an Octopus might provide inspiration for these future business systems.

A BCG roundtable discussion for chief information officers and other digital leaders explored trends in data and AI. They explored the three-pressure points that need consideration, and it largely comes down to people.

BCG-MIT Report: Artificial Intelligence in Business Gets Real

What Insight Did You Find Most Surprising from this Year’s Research?

What Insight Did You Find Most Surprising from this Year’s Research?

How Do Pioneers Approach AI at Scale as the Next Step of Digital Transformation?

How Do Pioneers Differ in Their AI Adoption from Less Mature Organizations?

Companies can scale AI successfully by increasing the number of AI pilots

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